Detection and surgical treatment of the early stages of tissue malignancy are usually curative. In contrast, diagnosis and treatment in late stages often have deadly results.
Histopathological examination, which is a diagnostic method of a tissue specimen, is one of the most important medical tool in process of diagnosis. The histopathological diagnosis of several cancer types is based upon architectural (symmetry, circumscription, maturation, nests, arrangement, distribution), cytological (atypicality, mitosis, necrosis) and other cancer-specific criteria. Nowadays, pathologists know the criteria list that has to be checked by the pathologist in order to diagnose the specimen. The pathological criteria are crucial in order to establish accurate diagnosis and prognosis.
A great deal of research has focused on creating content-based image retrieval (CBIR) systems to assist physicians in analyzing medical image data. A CBIR system relies on a similarity metric to retrieve images from a database. The metric used in most systems is a linear distance measure, but because most systems use a large number of features or dimensions, it is common to use manifold learning (ML) methods to map the data into a low-dimensional space. Images that are similar in a high dimensional space will be mapped close together in the transformed space, preserving object similarities. Although many ML methods have been developed over the years, most CBIR systems employ principal component analysis. A CBIR system was proposed for histopathology in that used color histograms, texture, and Fourier coefficients to describe the content of histological images from various malignancies, using a weighted cosine measure to determine image similarity. However, quantitative evaluation of the system with different feature sets and ML methods was not done.
In view of the above, there is an urgent need for a systematic medical decision support system for the examination of histopathological tissue images and diagnosis of benign and malignant pathologies while being able to provide prognosis and select appropriate treatment modalities.